Contributing¶

Contributions are welcome! LLMFS is an early-stage project and there are many ways to help.

Getting Started¶

# Fork and clone
git clone https://github.com/viditraj/llmfs.git
cd llmfs

# Create a branch
git checkout -b feature/my-improvement

# Install in editable mode with dev dependencies
pip install -e ".[dev,mcp,openai,langchain]"

# Run the test suite
pytest --cov=llmfs --cov-report=term-missing

# Run linting
ruff check llmfs/ tests/

Running Tests¶

# All tests
pytest

# Specific module
pytest tests/test_filesystem.py -v

# With coverage report
pytest --cov=llmfs --cov-report=html
open htmlcov/index.html

# Fast: skip slow embedding tests
pytest -m "not slow"

Areas for Contribution¶

• New embedders -- Add adapters for Cohere, Mistral, or local Ollama models
• Retrieval improvements -- Better score fusion, cross-encoder reranking
• MQL extensions -- Additional condition types, subqueries, aggregations
• Graph algorithms -- PageRank-based memory importance, community detection
• Streaming support -- Streaming writes for real-time transcript ingestion
• Windows FUSE -- WinFsp-based FUSE mount for Windows
• UI -- A web dashboard for browsing and editing memories

Submitting a Pull Request¶

1. Fork the repository and create a feature branch from main
2. Write tests for any new behavior -- we target 90%+ coverage
3. Run the full suite -- pytest && ruff check .
4. Update the README if you're adding a user-facing feature
5. Open a PR with a clear description of what the change does and why

Reporting Issues¶

Use the GitHub issue tracker. For bugs, please include:

• LLMFS version (pip show llmfs)
• Python version
• OS
• Minimal reproduction script